Air Quality Measure based on AQI

• An air quality index (AQI) is used by government agencies to communicate to the public how polluted the air currently is or how polluted it is forecast to become. Public health risks increase as the AQI rises.

• There are six AQI categories, namely Good, Satisfactory, Moderately polluted, Poor, Very Poor, and Severe. The proposed AQI will consider eight pollutants (PM10, PM2.5, NO2, SO2, CO, O3, NH3, and Pb) for which short-term (up to 24-hourly averaging period) National Ambient Air Quality Standards are prescribed.

• Based on the measured ambient concentrations, corresponding standards and likely health impact, a sub-index is calculated for each of these pollutants. The worst sub-index reflects overall AQI. Likely health impacts for different AQI categories and pollutants have also been suggested, with primary inputs from the medical experts in the group.

• The AQI values and corresponding ambient concentrations (health breakpoints) as well as associated likely health impacts for the identified eight pollutants are as follows:

• Associated Health Impacts

About The Project

• In this project we mainly focused on cleaning of data and tried to interpret various conclusions and visualisations from the collected data by using various libraries.

• We tried to visualise the yearly data of every pollutant, tried to find the most polluted and the least polluted city based on the data - station wise as well as city wise.

• At last we worked on a hypothesis testing, which works around the quality of air before and after COVID-19.

Libraries

Data Collection

Data collected from: https://www.kaggle.com/rohanrao/air-quality-data-in-india

Data Preprocessing and Cleaning

Cleaning of Stations' Data to remove the missing values

	StationId	StationName	City	State	Status
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	Active
1	AP002	Anand Kala Kshetram, Rajamahendravaram - APPCB	Rajamahendravaram	Andhra Pradesh	NaN
2	AP003	Tirumala, Tirupati - APPCB	Tirupati	Andhra Pradesh	NaN
3	AP004	PWD Grounds, Vijayawada - APPCB	Vijayawada	Andhra Pradesh	NaN
4	AP005	GVM Corporation, Visakhapatnam - APPCB	Visakhapatnam	Andhra Pradesh	Active
...	...	...	...	...	...
225	WB010	Jadavpur, Kolkata - WBPCB	Kolkata	West Bengal	Active
226	WB011	Rabindra Bharati University, Kolkata - WBPCB	Kolkata	West Bengal	Active
227	WB012	Rabindra Sarobar, Kolkata - WBPCB	Kolkata	West Bengal	Active
228	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	Active
229	WB014	Ward-32 Bapupara, Siliguri - WBPCB	Siliguri	West Bengal	NaN

230 rows × 5 columns

	StationId	StationName	City	State	Status
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	Active
4	AP005	GVM Corporation, Visakhapatnam - APPCB	Visakhapatnam	Andhra Pradesh	Active
5	AS001	Railway Colony, Guwahati - APCB	Guwahati	Assam	Active
10	BR005	DRM Office Danapur, Patna - BSPCB	Patna	Bihar	Active
11	BR006	Govt. High School Shikarpur, Patna - BSPCB	Patna	Bihar	Active
...	...	...	...	...	...
224	WB009	Fort William, Kolkata - WBPCB	Kolkata	West Bengal	Active
225	WB010	Jadavpur, Kolkata - WBPCB	Kolkata	West Bengal	Active
226	WB011	Rabindra Bharati University, Kolkata - WBPCB	Kolkata	West Bengal	Active
227	WB012	Rabindra Sarobar, Kolkata - WBPCB	Kolkata	West Bengal	Active
228	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	Active

133 rows × 5 columns

	StationId	StationName	City	State
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh
1	AP005	GVM Corporation, Visakhapatnam - APPCB	Visakhapatnam	Andhra Pradesh
2	AS001	Railway Colony, Guwahati - APCB	Guwahati	Assam
3	BR005	DRM Office Danapur, Patna - BSPCB	Patna	Bihar
4	BR006	Govt. High School Shikarpur, Patna - BSPCB	Patna	Bihar
...	...	...	...	...
128	WB009	Fort William, Kolkata - WBPCB	Kolkata	West Bengal
129	WB010	Jadavpur, Kolkata - WBPCB	Kolkata	West Bengal
130	WB011	Rabindra Bharati University, Kolkata - WBPCB	Kolkata	West Bengal
131	WB012	Rabindra Sarobar, Kolkata - WBPCB	Kolkata	West Bengal
132	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal

133 rows × 4 columns

Merging two dataframes i.e. station_day and stations on basis of StationId.

	StationId	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	AQI_Bucket
0	AP001	2017-11-24	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	0.17	5.92	0.10	NaN	NaN
1	AP001	2017-11-25	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	0.20	6.50	0.06	184.0	Moderate
2	AP001	2017-11-26	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	0.22	7.95	0.08	197.0	Moderate
3	AP001	2017-11-27	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	0.29	7.63	0.12	198.0	Moderate
4	AP001	2017-11-28	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	0.17	5.02	0.07	188.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
108030	WB013	2020-06-27	8.65	16.46	NaN	NaN	NaN	NaN	0.69	4.36	30.59	1.32	7.26	NaN	50.0	Good
108031	WB013	2020-06-28	11.80	18.47	NaN	NaN	NaN	NaN	0.68	3.49	38.95	1.42	7.92	NaN	65.0	Satisfactory
108032	WB013	2020-06-29	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	3.52	8.64	NaN	63.0	Satisfactory
108033	WB013	2020-06-30	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	1.86	8.40	NaN	57.0	Satisfactory
108034	WB013	2020-07-01	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	1.31	7.39	NaN	59.0	Satisfactory

108035 rows × 16 columns

	StationId	StationName	City	State	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	AQI_Bucket
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-24	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	0.17	5.92	0.10	NaN	NaN
1	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-25	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	0.20	6.50	0.06	184.0	Moderate
2	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-26	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	0.22	7.95	0.08	197.0	Moderate
3	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-27	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	0.29	7.63	0.12	198.0	Moderate
4	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-28	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	0.17	5.02	0.07	188.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
107706	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-27	8.65	16.46	NaN	NaN	NaN	NaN	0.69	4.36	30.59	1.32	7.26	NaN	50.0	Good
107707	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-28	11.80	18.47	NaN	NaN	NaN	NaN	0.68	3.49	38.95	1.42	7.92	NaN	65.0	Satisfactory
107708	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-29	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	3.52	8.64	NaN	63.0	Satisfactory
107709	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-30	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	1.86	8.40	NaN	57.0	Satisfactory
107710	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-07-01	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	1.31	7.39	NaN	59.0	Satisfactory

107711 rows × 19 columns

	StationId	StationName	City	State	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-24	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	0.17	5.92	0.10	184.0	Moderate
1	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-25	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	0.20	6.50	0.06	184.0	Moderate
2	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-26	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	0.22	7.95	0.08	197.0	Moderate
3	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-27	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	0.29	7.63	0.12	198.0	Moderate
4	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-28	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	0.17	5.02	0.07	188.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
107706	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-27	8.65	16.46	NaN	NaN	NaN	NaN	0.69	4.36	30.59	1.32	7.26	NaN	50.0	Good
107707	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-28	11.80	18.47	13.65	200.87	214.20	11.40	0.68	3.49	38.95	1.42	7.92	NaN	65.0	Satisfactory
107708	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-29	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	3.52	8.64	NaN	63.0	Satisfactory
107709	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-30	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	1.86	8.40	NaN	57.0	Satisfactory
107710	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-07-01	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	1.31	7.39	NaN	59.0	Satisfactory

107711 rows × 19 columns

Cleaning of Cities' Data to remove the missing values

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	AQI_Bucket
0	Ahmedabad	2015-01-01	NaN	NaN	0.92	18.22	17.15	NaN	0.92	27.64	133.36	0.00	0.02	0.00	NaN	NaN
1	Ahmedabad	2015-01-02	NaN	NaN	0.97	15.69	16.46	NaN	0.97	24.55	34.06	3.68	5.50	3.77	NaN	NaN
2	Ahmedabad	2015-01-03	NaN	NaN	17.40	19.30	29.70	NaN	17.40	29.07	30.70	6.80	16.40	2.25	NaN	NaN
3	Ahmedabad	2015-01-04	NaN	NaN	1.70	18.48	17.97	NaN	1.70	18.59	36.08	4.43	10.14	1.00	NaN	NaN
4	Ahmedabad	2015-01-05	NaN	NaN	22.10	21.42	37.76	NaN	22.10	39.33	39.31	7.01	18.89	2.78	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29526	Visakhapatnam	2020-06-27	15.02	50.94	7.68	25.06	19.54	12.47	0.47	8.55	23.30	2.24	12.07	0.73	41.0	Good
29527	Visakhapatnam	2020-06-28	24.38	74.09	3.42	26.06	16.53	11.99	0.52	12.72	30.14	0.74	2.21	0.38	70.0	Satisfactory
29528	Visakhapatnam	2020-06-29	22.91	65.73	3.45	29.53	18.33	10.71	0.48	8.42	30.96	0.01	0.01	0.00	68.0	Satisfactory
29529	Visakhapatnam	2020-06-30	16.64	49.97	4.05	29.26	18.80	10.03	0.52	9.84	28.30	0.00	0.00	0.00	54.0	Satisfactory
29530	Visakhapatnam	2020-07-01	15.00	66.00	0.40	26.85	14.05	5.20	0.59	2.10	17.05	NaN	NaN	NaN	50.0	Good

29531 rows × 16 columns

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
0	Ahmedabad	2015-01-01	NaN	NaN	0.92	18.22	17.15	NaN	0.92	27.64	133.36	0.00	0.02	0.00	NaN	NaN
1	Ahmedabad	2015-01-02	NaN	NaN	0.97	15.69	16.46	NaN	0.97	24.55	34.06	3.68	5.50	3.77	NaN	NaN
2	Ahmedabad	2015-01-03	NaN	NaN	17.40	19.30	29.70	NaN	17.40	29.07	30.70	6.80	16.40	2.25	NaN	NaN
3	Ahmedabad	2015-01-04	NaN	NaN	1.70	18.48	17.97	NaN	1.70	18.59	36.08	4.43	10.14	1.00	NaN	NaN
4	Ahmedabad	2015-01-05	NaN	NaN	22.10	21.42	37.76	NaN	22.10	39.33	39.31	7.01	18.89	2.78	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29526	Visakhapatnam	2020-06-27	15.02	50.94	7.68	25.06	19.54	12.47	0.47	8.55	23.30	2.24	12.07	0.73	41.0	Good
29527	Visakhapatnam	2020-06-28	24.38	74.09	3.42	26.06	16.53	11.99	0.52	12.72	30.14	0.74	2.21	0.38	70.0	Satisfactory
29528	Visakhapatnam	2020-06-29	22.91	65.73	3.45	29.53	18.33	10.71	0.48	8.42	30.96	0.01	0.01	0.00	68.0	Satisfactory
29529	Visakhapatnam	2020-06-30	16.64	49.97	4.05	29.26	18.80	10.03	0.52	9.84	28.30	0.00	0.00	0.00	54.0	Satisfactory
29530	Visakhapatnam	2020-07-01	15.00	66.00	0.40	26.85	14.05	5.20	0.59	2.10	17.05	NaN	NaN	NaN	50.0	Good

29531 rows × 16 columns

Data Visualisation

Descriptive Analysis of Stations_day

(107711, 19)

Calculating the number of null values in each columns.

StationId          0
StationName        0
City               0
State              0
Date               0
PM2.5          20417
PM10           41789
NO             15629
NO2            15058
NOx            14346
NH3            47245
CO             11386
SO2            23922
O3             24213
Benzene        30164
Toluene        37453
Xylene         84595
AQI            18958
Air_quality    18958
dtype: int64

Using the missingo library for getting the visual interpretation of missing values, so that we can replace it with some other values.

<AxesSubplot:>

Your selected dataframe has 19 columns.
There are 14 columns that have missing values.

	Missing Values	% of Total Values
Xylene	84595	78.500000
NH3	47245	43.900000
PM10	41789	38.800000
Toluene	37453	34.800000
Benzene	30164	28.000000
O3	24213	22.500000
SO2	23922	22.200000
PM2.5	20417	19.000000
AQI	18958	17.600000
Air_quality	18958	17.600000
NO	15629	14.500000
NO2	15058	14.000000
NOx	14346	13.300000
CO	11386	10.600000

<class 'pandas.core.frame.DataFrame'>
Int64Index: 107711 entries, 0 to 107710
Data columns (total 19 columns):
 #   Column       Non-Null Count   Dtype         
---  ------       --------------   -----         
 0   StationId    107711 non-null  object        
 1   StationName  107711 non-null  object        
 2   City         107711 non-null  object        
 3   State        107711 non-null  object        
 4   Date         107711 non-null  datetime64[ns]
 5   PM2.5        87294 non-null   float64       
 6   PM10         65922 non-null   float64       
 7   NO           92082 non-null   float64       
 8   NO2          92653 non-null   float64       
 9   NOx          93365 non-null   float64       
 10  NH3          60466 non-null   float64       
 11  CO           96325 non-null   float64       
 12  SO2          83789 non-null   float64       
 13  O3           83498 non-null   float64       
 14  Benzene      77547 non-null   float64       
 15  Toluene      70258 non-null   float64       
 16  Xylene       23116 non-null   float64       
 17  AQI          88753 non-null   float64       
 18  Air_quality  88753 non-null   object        
dtypes: datetime64[ns](1), float64(13), object(5)
memory usage: 16.4+ MB

column name:StationId   unique values:108
column name:StationName   unique values:108
column name:City   unique values:24
column name:State   unique values:21
column name:Date   unique values:2009
column name:PM2.5   unique values:22392
column name:PM10   unique values:29547
column name:NO   unique values:11914
column name:NO2   unique values:12051
column name:NOx   unique values:15585
column name:NH3   unique values:9112
column name:CO   unique values:2353
column name:SO2   unique values:5802
column name:O3   unique values:11161
column name:Benzene   unique values:3018
column name:Toluene   unique values:8714
column name:Xylene   unique values:1893
column name:AQI   unique values:931
column name:Air_quality   unique values:7

Visualising the yearly data of every pollutant

We're making a column which only comprises of Benzene + Toluene + Xylene because of its same biological nature.

We're making a Patriculate_Matter only column.

	StationId	StationName	City	State	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	AQI	Air_quality	BTX	Particulate_Matter
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-24	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	184.0	Moderate	6.19	187.11
1	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-25	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	184.0	Moderate	6.76	205.90
2	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-26	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	197.0	Moderate	8.25	207.38
3	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-27	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	198.0	Moderate	8.04	224.08
4	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-28	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	188.0	Moderate	5.26	168.27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
107706	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-27	8.65	16.46	NaN	NaN	NaN	NaN	0.69	4.36	30.59	50.0	Good	NaN	25.11
107707	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-28	11.80	18.47	13.65	200.87	214.20	11.40	0.68	3.49	38.95	65.0	Satisfactory	NaN	30.27
107708	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-29	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	63.0	Satisfactory	NaN	50.86
107709	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-30	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	57.0	Satisfactory	NaN	55.37
107710	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-07-01	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	59.0	Satisfactory	NaN	47.00

107711 rows × 18 columns

Descriptive Analysis of city_day

(29531, 16)

Calculating the number of null values in each columns.

City               0
Date               0
PM2.5           4321
PM10           10866
NO              3276
NO2             3278
NOx             3980
NH3            10061
CO              1745
SO2             3510
O3              3664
Benzene         5298
Toluene         7739
Xylene         17878
AQI             4174
Air_quality     4174
dtype: int64

Using the missingo library for getting the viusal interpretation of missing values, so that we can replace it with some other values.

<AxesSubplot:>

Your selected dataframe has 16 columns.
There are 14 columns that have missing values.

	Missing Values	% of Total Values
Xylene	17878	60.500000
PM10	10866	36.800000
NH3	10061	34.100000
Toluene	7739	26.200000
Benzene	5298	17.900000
PM2.5	4321	14.600000
AQI	4174	14.100000
Air_quality	4174	14.100000
NOx	3980	13.500000
O3	3664	12.400000
SO2	3510	11.900000
NO2	3278	11.100000
NO	3276	11.100000
CO	1745	5.900000

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 29531 entries, 0 to 29530
Data columns (total 16 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   City         29531 non-null  object        
 1   Date         29531 non-null  datetime64[ns]
 2   PM2.5        25210 non-null  float64       
 3   PM10         18665 non-null  float64       
 4   NO           26255 non-null  float64       
 5   NO2          26253 non-null  float64       
 6   NOx          25551 non-null  float64       
 7   NH3          19470 non-null  float64       
 8   CO           27786 non-null  float64       
 9   SO2          26021 non-null  float64       
 10  O3           25867 non-null  float64       
 11  Benzene      24233 non-null  float64       
 12  Toluene      21792 non-null  float64       
 13  Xylene       11653 non-null  float64       
 14  AQI          25357 non-null  float64       
 15  Air_quality  25357 non-null  object        
dtypes: datetime64[ns](1), float64(13), object(2)
memory usage: 3.6+ MB

column name:City   unique values:26
column name:Date   unique values:2009
column name:PM2.5   unique values:11717
column name:PM10   unique values:12572
column name:NO   unique values:5777
column name:NO2   unique values:7405
column name:NOx   unique values:8157
column name:NH3   unique values:5923
column name:CO   unique values:1780
column name:SO2   unique values:4762
column name:O3   unique values:7700
column name:Benzene   unique values:1874
column name:Toluene   unique values:3609
column name:Xylene   unique values:1562
column name:AQI   unique values:830
column name:Air_quality   unique values:7

Visualising the yearly data of every pollutant

We're making a column which only comprises of Benzene + Toluene + Xylene because of its same biological nature.

We're making a Patriculate_Matter only column.

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	AQI	Air_quality	BTX	Particulate_Matter
0	Ahmedabad	2015-01-01	NaN	NaN	0.92	18.22	17.15	NaN	0.92	27.64	133.36	NaN	NaN	0.02	NaN
1	Ahmedabad	2015-01-02	NaN	NaN	0.97	15.69	16.46	NaN	0.97	24.55	34.06	NaN	NaN	12.95	NaN
2	Ahmedabad	2015-01-03	NaN	NaN	17.40	19.30	29.70	NaN	17.40	29.07	30.70	NaN	NaN	25.45	NaN
3	Ahmedabad	2015-01-04	NaN	NaN	1.70	18.48	17.97	NaN	1.70	18.59	36.08	NaN	NaN	15.57	NaN
4	Ahmedabad	2015-01-05	NaN	NaN	22.10	21.42	37.76	NaN	22.10	39.33	39.31	NaN	NaN	28.68	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29526	Visakhapatnam	2020-06-27	15.02	50.94	7.68	25.06	19.54	12.47	0.47	8.55	23.30	41.0	Good	15.04	65.96
29527	Visakhapatnam	2020-06-28	24.38	74.09	3.42	26.06	16.53	11.99	0.52	12.72	30.14	70.0	Satisfactory	3.33	98.47
29528	Visakhapatnam	2020-06-29	22.91	65.73	3.45	29.53	18.33	10.71	0.48	8.42	30.96	68.0	Satisfactory	0.02	88.64
29529	Visakhapatnam	2020-06-30	16.64	49.97	4.05	29.26	18.80	10.03	0.52	9.84	28.30	54.0	Satisfactory	0.00	66.61
29530	Visakhapatnam	2020-07-01	15.00	66.00	0.40	26.85	14.05	5.20	0.59	2.10	17.05	50.0	Good	NaN	81.00

29531 rows × 15 columns

Data Statistics

Most and Least Polluted Stations based on the given dataset

Most Polluted

	StationId	StationName	City	State	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	AQI	Air_quality	BTX	Particulate_Matter
Date
2017-11-24	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	184.0	Moderate	6.19	187.11
2017-11-25	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	184.0	Moderate	6.76	205.90
2017-11-26	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	197.0	Moderate	8.25	207.38
2017-11-27	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	198.0	Moderate	8.04	224.08
2017-11-28	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	188.0	Moderate	5.26	168.27
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-06-27	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	8.65	16.46	NaN	NaN	NaN	NaN	0.69	4.36	30.59	50.0	Good	NaN	25.11
2020-06-28	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	11.80	18.47	13.65	200.87	214.20	11.40	0.68	3.49	38.95	65.0	Satisfactory	NaN	30.27
2020-06-29	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	63.0	Satisfactory	NaN	50.86
2020-06-30	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	57.0	Satisfactory	NaN	55.37
2020-07-01	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	59.0	Satisfactory	NaN	47.00

107711 rows × 17 columns

	StationName	PM2.5
0	Anand Vihar, Delhi - DPCC	152.350000
1	Talkatora District Industries Center, Lucknow - CPCB	134.690000
2	DTU, Delhi - CPCB	131.080000
3	IGSC Planetarium Complex, Patna - BSPCB	130.450000
4	Jahangirpuri, Delhi - DPCC	128.120000
5	Wazirpur, Delhi - DPCC	127.510000
6	Mundka, Delhi - DPCC	122.460000
7	Rohini, Delhi - DPCC	122.420000
8	Bawana, Delhi - DPCC	120.950000
9	Burari Crossing, Delhi - IMD	120.820000

	StationName	PM10
0	Anand Vihar, Delhi - DPCC	358.120000
1	Wazirpur, Delhi - DPCC	277.420000
2	Dwarka-Sector 8, Delhi - DPCC	276.470000
3	Mundka, Delhi - DPCC	269.130000
4	Jahangirpuri, Delhi - DPCC	259.450000
5	Sirifort, Delhi - CPCB	252.480000
6	Rohini, Delhi - DPCC	247.870000
7	NSIT Dwarka, Delhi - CPCB	242.770000
8	R K Puram, Delhi - DPCC	242.410000
9	DTU, Delhi - CPCB	236.060000

	StationName	Particulate_Matter
0	Anand Vihar, Delhi - DPCC	509.740000
1	Wazirpur, Delhi - DPCC	405.510000
2	Mundka, Delhi - DPCC	391.590000
3	Jahangirpuri, Delhi - DPCC	387.750000
4	Dwarka-Sector 8, Delhi - DPCC	379.790000
5	Rohini, Delhi - DPCC	370.440000
6	R K Puram, Delhi - DPCC	361.170000
7	Bawana, Delhi - DPCC	354.700000
8	Sirifort, Delhi - CPCB	349.750000
9	DTU, Delhi - CPCB	346.270000

	StationName	NO
0	Samanpura, Patna - BSPCB	124.000000
1	Anand Vihar, Delhi - DPCC	90.860000
2	Pusa, Delhi - DPCC	73.060000
3	DRM Office Danapur, Patna - BSPCB	64.930000
4	Major Dhyan Chand National Stadium, Delhi - DPCC	57.780000
5	R K Puram, Delhi - DPCC	54.420000
6	Chhatrapati Shivaji Intl. Airport (T2), Mumbai - MPCB	53.380000
7	Jawaharlal Nehru Stadium, Delhi - DPCC	52.570000
8	ITO, Delhi - CPCB	50.600000
9	Sirifort, Delhi - CPCB	46.990000

	StationName	NO2
0	Anand Vihar, Delhi - DPCC	88.720000
1	Punjabi Bagh, Delhi - DPCC	73.280000
2	Rajbansi Nagar, Patna - BSPCB	65.900000
3	Jahangirpuri, Delhi - DPCC	65.860000
4	Jawaharlal Nehru Stadium, Delhi - DPCC	63.340000
5	R K Puram, Delhi - DPCC	63.010000
6	Pusa, Delhi - DPCC	59.570000
7	Sirifort, Delhi - CPCB	58.860000
8	Maninagar, Ahmedabad - GPCB	58.850000
9	Major Dhyan Chand National Stadium, Delhi - DPCC	58.720000

	StationName	NOx
0	Anand Vihar, Delhi - DPCC	148.780000
1	Samanpura, Patna - BSPCB	141.460000
2	East Arjun Nagar, Delhi - CPCB	121.800000
3	Pusa, Delhi - DPCC	91.230000
4	R K Puram, Delhi - DPCC	86.360000
5	Chhatrapati Shivaji Intl. Airport (T2), Mumbai - MPCB	84.670000
6	Major Dhyan Chand National Stadium, Delhi - DPCC	82.100000
7	Jawaharlal Nehru Stadium, Delhi - DPCC	81.310000
8	Sion, Mumbai - MPCB	74.310000
9	Victoria, Kolkata - WBPCB	73.730000

	StationName	NH3
0	Manali, Chennai - CPCB	65.360000
1	Anand Vihar, Delhi - DPCC	55.780000
2	Jahangirpuri, Delhi - DPCC	55.670000
3	Rohini, Delhi - DPCC	53.010000
4	ITO, Delhi - CPCB	52.060000
5	IGSC Planetarium Complex, Patna - BSPCB	51.240000
6	NSIT Dwarka, Delhi - CPCB	48.870000
7	Patparganj, Delhi - DPCC	48.850000
8	Shadipur, Delhi - CPCB	45.680000
9	Mundka, Delhi - DPCC	45.510000

	StationName	CO
0	Maninagar, Ahmedabad - GPCB	22.360000
1	BWSSB Kadabesanahalli, Bengaluru - CPCB	3.580000
2	Shadipur, Delhi - CPCB	3.480000
3	Peenya, Bengaluru - CPCB	3.010000
4	NSIT Dwarka, Delhi - CPCB	2.840000
5	Central School, Lucknow - CPCB	2.310000
6	Lalbagh, Lucknow - CPCB	2.260000
7	Anand Vihar, Delhi - DPCC	2.200000
8	ITO, Delhi - CPCB	2.120000
9	Alandur Bus Depot, Chennai - CPCB	1.950000

	StationName	SO2
0	Maninagar, Ahmedabad - GPCB	55.250000
1	Tata Stadium, Jorapokhar - JSPCB	34.640000
2	Talcher Coalfields,Talcher - OSPCB	28.410000
3	Pusa, Delhi - IMD	27.630000
4	Lodhi Road, Delhi - IMD	23.250000
5	North Campus, DU, Delhi - IMD	23.250000
6	IGSC Planetarium Complex, Patna - BSPCB	22.980000
7	R K Puram, Delhi - DPCC	20.690000
8	Punjabi Bagh, Delhi - DPCC	20.080000
9	Alipur, Delhi - DPCC	19.860000

	StationName	O3
0	Punjabi Bagh, Delhi - DPCC	230.100000
1	Sector-51, Gurugram - HSPCB	80.750000
2	Manali Village, Chennai - TNPCB	69.610000
3	T T Nagar, Bhopal - MPPCB	59.940000
4	R K Puram, Delhi - DPCC	54.780000
5	Shastri Nagar, Jaipur - RSPCB	53.200000
6	Teri Gram, Gurugram - HSPCB	52.960000
7	Adarsh Nagar, Jaipur - RSPCB	51.530000
8	Sirifort, Delhi - CPCB	49.010000
9	Hombegowda Nagar, Bengaluru - KSPCB	47.610000

	StationName	BTX
0	Jadavpur, Kolkata - WBPCB	220.430000
1	Talkatora District Industries Center, Lucknow - CPCB	56.030000
2	Maninagar, Ahmedabad - GPCB	37.630000
3	Burari Crossing, Delhi - IMD	33.190000
4	IDA Pashamylaram, Hyderabad - TSPCB	31.310000
5	Fort William, Kolkata - WBPCB	29.760000
6	Bidhannagar, Kolkata - WBPCB	27.680000
7	Ballygunge, Kolkata - WBPCB	26.130000
8	Mandir Marg, Delhi - DPCC	20.830000
9	Teri Gram, Gurugram - HSPCB	20.500000

Least Polluted

	StationName	PM2.5
0	City Railway Station, Bengaluru - KSPCB	9.000000
1	East Arjun Nagar, Delhi - CPCB	11.110000
2	Sikulpuikawn, Aizawl - Mizoram PCB	16.850000
3	Manali Village, Chennai - TNPCB	24.480000
4	Plammoodu, Thiruvananthapuram - Kerala PCB	27.220000
5	Hombegowda Nagar, Bengaluru - KSPCB	27.520000
6	Hebbal, Bengaluru - KSPCB	28.930000
7	Kariavattom, Thiruvananthapuram - Kerala PCB	28.980000
8	SIDCO Kurichi, Coimbatore - TNPCB	29.090000
9	Borivali East, Mumbai - MPCB	29.290000

	StationName	PM10
0	East Arjun Nagar, Delhi - CPCB	6.320000
1	Sikulpuikawn, Aizawl - Mizoram PCB	23.340000
2	Talkatora District Industries Center, Lucknow - CPCB	26.860000
3	SIDCO Kurichi, Coimbatore - TNPCB	37.740000
4	BWSSB Kadabesanahalli, Bengaluru - CPCB	40.750000
5	Lumpyngngad, Shillong - Meghalaya PCB	41.640000
6	Velachery Res. Area, Chennai - CPCB	43.490000
7	Alandur Bus Depot, Chennai - CPCB	48.550000
8	Kariavattom, Thiruvananthapuram - Kerala PCB	51.490000
9	Plammoodu, Thiruvananthapuram - Kerala PCB	52.190000

	StationName	Particulate_Matter
0	East Arjun Nagar, Delhi - CPCB	17.430000
1	City Railway Station, Bengaluru - KSPCB	40.000000
2	Sikulpuikawn, Aizawl - Mizoram PCB	40.190000
3	Sanegurava Halli, Bengaluru - KSPCB	45.960000
4	Alandur Bus Depot, Chennai - CPCB	56.320000
5	Talkatora District Industries Center, Lucknow - CPCB	57.880000
6	Velachery Res. Area, Chennai - CPCB	60.770000
7	BWSSB Kadabesanahalli, Bengaluru - CPCB	62.450000
8	SIDCO Kurichi, Coimbatore - TNPCB	66.900000
9	Lumpyngngad, Shillong - Meghalaya PCB	72.880000

	StationName	NO
0	Lumpyngngad, Shillong - Meghalaya PCB	0.920000
1	Bollaram Industrial Area, Hyderabad - TSPCB	2.960000
2	Borivali East, Mumbai - MPCB	3.260000
3	Plammoodu, Thiruvananthapuram - Kerala PCB	3.410000
4	ICRISAT Patancheru, Hyderabad - TSPCB	3.550000
5	Hombegowda Nagar, Bengaluru - KSPCB	3.570000
6	Sector-51, Gurugram - HSPCB	3.810000
7	Secretariat, Amaravati - APPCB	4.450000
8	Peenya, Bengaluru - CPCB	4.510000
9	Powai, Mumbai - MPCB	4.770000

	StationName	NO2
0	Sikulpuikawn, Aizawl - Mizoram PCB	0.390000
1	Lumpyngngad, Shillong - Meghalaya PCB	2.770000
2	Borivali East, Mumbai - MPCB	4.650000
3	Teri Gram, Gurugram - HSPCB	4.910000
4	Manali Village, Chennai - TNPCB	9.110000
5	Plammoodu, Thiruvananthapuram - Kerala PCB	9.190000
6	Tata Stadium, Jorapokhar - JSPCB	9.370000
7	Govt. High School Shikarpur, Patna - BSPCB	9.620000
8	Powai, Mumbai - MPCB	10.390000
9	Sector-25, Chandigarh - CPCC	11.630000

	StationName	NOx
0	Lumpyngngad, Shillong - Meghalaya PCB	1.000000
1	Teri Gram, Gurugram - HSPCB	5.960000
2	Tata Stadium, Jorapokhar - JSPCB	7.410000
3	Plammoodu, Thiruvananthapuram - Kerala PCB	7.550000
4	Borivali East, Mumbai - MPCB	7.710000
5	Govt. High School Shikarpur, Patna - BSPCB	9.440000
6	Sector-51, Gurugram - HSPCB	11.210000
7	ICRISAT Patancheru, Hyderabad - TSPCB	11.220000
8	Bollaram Industrial Area, Hyderabad - TSPCB	12.140000
9	Sikulpuikawn, Aizawl - Mizoram PCB	12.610000

	StationName	NH3
0	Lumpyngngad, Shillong - Meghalaya PCB	2.810000
1	Plammoodu, Thiruvananthapuram - Kerala PCB	5.030000
2	Worli, Mumbai - MPCB	6.560000
3	Tata Stadium, Jorapokhar - JSPCB	7.000000
4	Bandra, Mumbai - MPCB	7.160000
5	Colaba, Mumbai - MPCB	8.020000
6	Kariavattom, Thiruvananthapuram - Kerala PCB	8.040000
7	Borivali East, Mumbai - MPCB	8.170000
8	Nishant Ganj, Lucknow - UPPCB	8.910000
9	SIDCO Kurichi, Coimbatore - TNPCB	9.310000

	StationName	CO
0	Lumpyngngad, Shillong - Meghalaya PCB	0.240000
1	Sikulpuikawn, Aizawl - Mizoram PCB	0.280000
2	Borivali East, Mumbai - MPCB	0.370000
3	Bollaram Industrial Area, Hyderabad - TSPCB	0.410000
4	Worli, Mumbai - MPCB	0.410000
5	Kurla, Mumbai - MPCB	0.420000
6	Colaba, Mumbai - MPCB	0.460000
7	Sanegurava Halli, Bengaluru - KSPCB	0.480000
8	ICRISAT Patancheru, Hyderabad - TSPCB	0.490000
9	Sion, Mumbai - MPCB	0.490000

	StationName	SO2
0	Kariavattom, Thiruvananthapuram - Kerala PCB	3.230000
1	BWSSB Kadabesanahalli, Bengaluru - CPCB	3.810000
2	Sanegurava Halli, Bengaluru - KSPCB	3.880000
3	DRM Office Danapur, Patna - BSPCB	4.780000
4	Silk Board, Bengaluru - KSPCB	4.820000
5	Zoo Park, Hyderabad - TSPCB	4.830000
6	Patparganj, Delhi - DPCC	4.840000
7	Central University, Hyderabad - TSPCB	5.150000
8	Jayanagar 5th Block, Bengaluru - KSPCB	5.270000
9	Muradpur, Patna - BSPCB	5.310000

	StationName	O3
0	Sikulpuikawn, Aizawl - Mizoram PCB	3.570000
1	Sanegurava Halli, Bengaluru - KSPCB	6.290000
2	Govt. High School Shikarpur, Patna - BSPCB	7.060000
3	Muradpur, Patna - BSPCB	11.920000
4	Chhatrapati Shivaji Intl. Airport (T2), Mumbai - MPCB	12.920000
5	GM Office, Brajrajnagar - OSPCB	16.790000
6	Vasai West, Mumbai - MPCB	17.010000
7	Talcher Coalfields,Talcher - OSPCB	17.570000
8	Manali, Chennai - CPCB	18.850000
9	Borivali East, Mumbai - MPCB	19.020000

	StationName	BTX
0	T T Nagar, Bhopal - MPPCB	0.000000
1	Bandra, Mumbai - MPCB	0.030000
2	SIDCO Kurichi, Coimbatore - TNPCB	0.200000
3	Lodhi Road, Delhi - IMD	1.960000
4	North Campus, DU, Delhi - IMD	2.380000
5	CRRI Mathura Road, Delhi - IMD	2.770000
6	Bollaram Industrial Area, Hyderabad - TSPCB	2.960000
7	ICRISAT Patancheru, Hyderabad - TSPCB	3.440000
8	Pusa, Delhi - IMD	3.710000
9	Secretariat, Amaravati - APPCB	3.860000

Most and Least Polluted Cities based on the given dataset

Most Polluted

	City	PM2.5
0	Patna	123.110000
1	Gurugram	117.340000
2	Delhi	117.150000
3	Lucknow	109.940000
4	Ahmedabad	67.820000
5	Jorapokhar	64.670000
6	Brajrajnagar	64.360000
7	Kolkata	64.120000
8	Guwahati	63.940000
9	Talcher	61.010000

	City	PM10
0	Delhi	232.730000
1	Gurugram	192.490000
2	Talcher	165.290000
3	Jorapokhar	150.390000
4	Patna	126.910000
5	Brajrajnagar	124.940000
6	Jaipur	123.400000
7	Bhopal	119.210000
8	Guwahati	116.600000
9	Kolkata	115.260000

	City	NO
0	Kochi	71.370000
1	Delhi	38.980000
2	Patna	31.800000
3	Talcher	31.770000
4	Mumbai	31.560000
5	Kolkata	26.840000
6	Ernakulam	23.570000
7	Ahmedabad	22.590000
8	Guwahati	20.010000
9	Brajrajnagar	19.200000

	City	NO2
0	Ahmedabad	58.850000
1	Delhi	50.800000
2	Kolkata	40.300000
3	Patna	37.560000
4	Visakhapatnam	37.040000
5	Lucknow	33.220000
6	Jaipur	32.360000
7	Bhopal	31.290000
8	Coimbatore	28.970000
9	Hyderabad	28.430000

	City	NOx
0	Jorapokhar	99.990000
1	Kochi	68.410000
2	Kolkata	63.340000
3	Delhi	58.570000
4	Mumbai	55.180000
5	Ahmedabad	47.370000
6	Patna	46.110000
7	Guwahati	44.250000
8	Jaipur	39.650000
9	Amritsar	35.690000

	City	NH3
0	Chennai	63.400000
1	Delhi	41.990000
2	Brajrajnagar	36.960000
3	Chandigarh	30.600000
4	Lucknow	29.220000
5	Ahmedabad	26.640000
6	Jaipur	26.470000
7	Gurugram	26.210000
8	Aizawl	22.310000
9	Bengaluru	22.160000

	City	CO
0	Ahmedabad	22.360000
1	Lucknow	2.130000
2	Delhi	1.980000
3	Talcher	1.850000
4	Bengaluru	1.840000
5	Brajrajnagar	1.790000
6	Ernakulam	1.630000
7	Patna	1.500000
8	Kochi	1.300000
9	Gurugram	1.260000

	City	SO2
0	Ahmedabad	55.250000
1	Jorapokhar	34.640000
2	Talcher	28.410000
3	Patna	22.020000
4	Kochi	17.600000
5	Delhi	15.900000
6	Mumbai	15.710000
7	Guwahati	14.660000
8	Amaravati	14.270000
9	Bhopal	13.080000

	City	O3
0	Bhopal	59.940000
1	Delhi	51.290000
2	Jaipur	46.600000
3	Ahmedabad	39.310000
4	Amaravati	38.130000
5	Visakhapatnam	37.600000
6	Patna	37.070000
7	Lucknow	36.990000
8	Thiruvananthapuram	34.520000
9	Gurugram	34.250000

	City	BTX
0	Kolkata	38.110000
1	Ahmedabad	37.630000
2	Delhi	26.780000
3	Thiruvananthapuram	22.350000
4	Patna	17.270000
5	Visakhapatnam	15.080000
6	Gurugram	14.640000
7	Amritsar	14.500000
8	Hyderabad	10.720000
9	Lucknow	10.410000

Least Polluted

	City	PM2.5
0	Aizawl	16.850000
1	Ernakulam	24.960000
2	Thiruvananthapuram	27.990000
3	Coimbatore	29.730000
4	Shillong	30.290000
5	Kochi	31.430000
6	Mumbai	35.260000
7	Bengaluru	36.090000
8	Amaravati	37.640000
9	Chandigarh	41.060000

	City	PM10
0	Aizawl	23.340000
1	Coimbatore	39.230000
2	Shillong	41.640000
3	Ernakulam	48.310000
4	Thiruvananthapuram	52.790000
5	Chennai	62.950000
6	Kochi	67.340000
7	Amaravati	76.310000
8	Bengaluru	83.590000
9	Chandigarh	85.660000

	City	NO
0	Shillong	0.920000
1	Thiruvananthapuram	3.440000
2	Amaravati	4.450000
3	Bhopal	7.020000
4	Coimbatore	7.530000
5	Hyderabad	7.830000
6	Chennai	9.190000
7	Bengaluru	9.400000
8	Aizawl	9.410000
9	Chandigarh	10.470000

	City	NO2
0	Aizawl	0.390000
1	Shillong	2.770000
2	Ernakulam	3.630000
3	Thiruvananthapuram	9.370000
4	Jorapokhar	9.370000
5	Chandigarh	11.630000
6	Guwahati	13.560000
7	Talcher	13.770000
8	Kochi	14.860000
9	Brajrajnagar	16.530000

	City	NOx
0	Shillong	1.000000
1	Thiruvananthapuram	8.160000
2	Aizawl	12.610000
3	Chandigarh	15.070000
4	Amaravati	15.390000
5	Chennai	17.660000
6	Hyderabad	19.460000
7	Bengaluru	19.700000
8	Bhopal	22.380000
9	Lucknow	22.460000

	City	NH3
0	Shillong	2.810000
1	Thiruvananthapuram	5.070000
2	Jorapokhar	7.000000
3	Kochi	7.980000
4	Coimbatore	9.400000
5	Visakhapatnam	10.970000
6	Guwahati	11.100000
7	Talcher	11.600000
8	Amaravati	12.030000
9	Mumbai	13.820000

	City	CO
0	Shillong	0.240000
1	Aizawl	0.280000
2	Amritsar	0.550000
3	Mumbai	0.570000
4	Hyderabad	0.590000
5	Amaravati	0.630000
6	Chandigarh	0.630000
7	Jorapokhar	0.650000
8	Guwahati	0.730000
9	Visakhapatnam	0.740000

	City	SO2
0	Ernakulam	3.180000
1	Bengaluru	5.510000
2	Thiruvananthapuram	5.650000
3	Shillong	6.620000
4	Aizawl	7.380000
5	Chennai	7.870000
6	Amritsar	8.130000
7	Kolkata	8.530000
8	Coimbatore	8.590000
9	Hyderabad	9.190000

	City	O3
0	Aizawl	3.570000
1	Kochi	3.820000
2	Ernakulam	5.960000
3	Brajrajnagar	16.850000
4	Talcher	17.570000
5	Chandigarh	20.050000
6	Amritsar	22.440000
7	Guwahati	25.060000
8	Shillong	27.690000
9	Coimbatore	28.820000

	City	BTX
0	Mumbai	0.030000
1	Ernakulam	2.010000
2	Amaravati	3.860000
3	Aizawl	6.190000
4	Guwahati	7.260000
5	Brajrajnagar	7.900000
6	Chandigarh	9.090000
7	Coimbatore	9.840000
8	Lucknow	10.410000
9	Hyderabad	10.720000

Data Interpretation

Stations

Filling the missing values with the help of median of each column.

	StationId	StationName	City	State	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-24	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	0.17	5.92	0.10	184.0	Moderate
1	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-25	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	0.20	6.50	0.06	184.0	Moderate
2	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-26	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	0.22	7.95	0.08	197.0	Moderate
3	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-27	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	0.29	7.63	0.12	198.0	Moderate
4	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-28	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	0.17	5.02	0.07	188.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
107706	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-27	8.65	16.46	NaN	NaN	NaN	NaN	0.69	4.36	30.59	1.32	7.26	NaN	50.0	Good
107707	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-28	11.80	18.47	13.65	200.87	214.20	11.40	0.68	3.49	38.95	1.42	7.92	NaN	65.0	Satisfactory
107708	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-29	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	3.52	8.64	NaN	63.0	Satisfactory
107709	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-30	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	1.86	8.40	NaN	57.0	Satisfactory
107710	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-07-01	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	1.31	7.39	NaN	59.0	Satisfactory

107711 rows × 19 columns

StationId          0
StationName        0
City               0
State              0
Date               0
PM2.5          20417
PM10           41789
NO             15629
NO2            15058
NOx            14346
NH3            47245
CO             11386
SO2            23922
O3             24213
Benzene        30164
Toluene        37453
Xylene         84595
AQI            18958
Air_quality    18958
dtype: int64

	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI
count	87294.000000	65922.000000	92082.000000	92653.000000	93365.000000	60466.000000	96325.000000	83789.000000	83498.000000	77547.000000	70258.000000	23116.000000	88753.000000
mean	80.344685	158.258377	23.065599	35.362506	41.214568	28.824049	1.628631	12.300694	38.221072	3.383664	15.552798	2.458169	179.803004
std	76.654693	123.416377	34.558080	29.746311	45.315124	24.998420	4.488459	13.196388	39.240139	11.300496	29.826612	6.734150	131.420900
min	0.020000	0.010000	0.010000	0.010000	0.000000	0.010000	0.000000	0.010000	0.010000	0.000000	0.000000	0.000000	8.000000
25%	31.940000	70.490000	4.822500	15.130000	13.960000	11.930000	0.530000	5.040000	18.930000	0.160000	0.710000	0.000000	86.000000
50%	56.010000	122.490000	10.270000	27.250000	26.620000	23.650000	0.910000	8.930000	30.880000	1.210000	4.380000	0.400000	133.000000
75%	100.000000	208.967500	24.840000	47.030000	50.400000	38.200000	1.450000	14.900000	47.220000	3.610000	17.620000	2.120000	254.000000
max	1000.000000	1000.000000	470.000000	448.050000	467.630000	418.900000	175.810000	195.650000	963.000000	455.030000	454.850000	170.370000	2049.000000

Replacing zeroes with median values.

StationId          0
StationName        0
City               0
State              0
Date               0
PM2.5              0
PM10               0
NO                 0
NO2                0
NOx             4878
NH3                0
CO              7484
SO2                0
O3                 0
Benzene        12876
Toluene        10550
Xylene          6146
AQI                0
Air_quality        0
dtype: int64

Checking the data via various methods for final visualisation

StationId      0
StationName    0
City           0
State          0
Date           0
PM2.5          0
PM10           0
NO             0
NO2            0
NOx            0
NH3            0
CO             0
SO2            0
O3             0
Benzene        0
Toluene        0
Xylene         0
AQI            0
Air_quality    0
dtype: int64

StationId      0
StationName    0
City           0
State          0
Date           0
PM2.5          0
PM10           0
NO             0
NO2            0
NOx            0
NH3            0
CO             0
SO2            0
O3             0
Benzene        0
Toluene        0
Xylene         0
AQI            0
Air_quality    0
dtype: int64

<class 'pandas.core.frame.DataFrame'>
Int64Index: 107711 entries, 0 to 107710
Data columns (total 19 columns):
 #   Column       Non-Null Count   Dtype         
---  ------       --------------   -----         
 0   StationId    107711 non-null  object        
 1   StationName  107711 non-null  object        
 2   City         107711 non-null  object        
 3   State        107711 non-null  object        
 4   Date         107711 non-null  datetime64[ns]
 5   PM2.5        107711 non-null  float64       
 6   PM10         107711 non-null  float64       
 7   NO           107711 non-null  float64       
 8   NO2          107711 non-null  float64       
 9   NOx          107711 non-null  float64       
 10  NH3          107711 non-null  float64       
 11  CO           107711 non-null  float64       
 12  SO2          107711 non-null  float64       
 13  O3           107711 non-null  float64       
 14  Benzene      107711 non-null  float64       
 15  Toluene      107711 non-null  float64       
 16  Xylene       107711 non-null  float64       
 17  AQI          107711 non-null  float64       
 18  Air_quality  107711 non-null  object        
dtypes: datetime64[ns](1), float64(13), object(5)
memory usage: 16.4+ MB

	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI
count	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000	107711.000000
mean	75.731960	144.381199	21.208942	34.228378	40.476284	26.554569	1.615894	11.552082	36.570829	2.919585	12.096830	0.864530	171.565300
std	69.664187	98.111580	32.268880	27.731846	41.719310	18.905102	4.233512	11.723084	34.684843	9.604304	24.500655	3.227418	120.620077
min	0.020000	0.010000	0.010000	0.010000	0.010000	0.010000	0.010000	0.010000	0.010000	0.010000	0.010000	0.010000	8.000000
25%	37.100000	101.960000	5.660000	16.930000	18.230000	21.050000	0.690000	6.080000	22.390000	1.210000	4.380000	0.400000	95.000000
50%	56.010000	122.490000	10.270000	27.250000	26.620000	23.650000	0.910000	8.930000	30.880000	1.210000	4.380000	0.400000	133.000000
75%	84.700000	146.915000	21.010000	42.770000	45.380000	26.330000	1.360000	12.670000	41.440000	2.410000	8.200000	0.400000	216.000000
max	1000.000000	1000.000000	470.000000	448.050000	467.630000	418.900000	175.810000	195.650000	963.000000	455.030000	454.850000	170.370000	2049.000000

array(['Secretariat, Amaravati - APPCB',
       'GVM Corporation, Visakhapatnam - APPCB',
       'Railway Colony, Guwahati - APCB',
       'DRM Office Danapur, Patna - BSPCB',
       'Govt. High School Shikarpur, Patna - BSPCB',
       'IGSC Planetarium Complex, Patna - BSPCB',
       'Muradpur, Patna - BSPCB', 'Rajbansi Nagar, Patna - BSPCB',
       'Samanpura, Patna - BSPCB', 'Sector-25, Chandigarh - CPCC',
       'Alipur, Delhi - DPCC', 'Anand Vihar, Delhi - DPCC',
       'Ashok Vihar, Delhi - DPCC', 'Aya Nagar, Delhi - IMD',
       'Bawana, Delhi - DPCC', 'Burari Crossing, Delhi - IMD',
       'CRRI Mathura Road, Delhi - IMD', 'DTU, Delhi - CPCB',
       'Dr. Karni Singh Shooting Range, Delhi - DPCC',
       'Dwarka-Sector 8, Delhi - DPCC', 'East Arjun Nagar, Delhi - CPCB',
       'IGI Airport (T3), Delhi - IMD',
       'IHBAS, Dilshad Garden, Delhi - CPCB', 'ITO, Delhi - CPCB',
       'Jahangirpuri, Delhi - DPCC',
       'Jawaharlal Nehru Stadium, Delhi - DPCC',
       'Lodhi Road, Delhi - IMD',
       'Major Dhyan Chand National Stadium, Delhi - DPCC',
       'Mandir Marg, Delhi - DPCC', 'Mundka, Delhi - DPCC',
       'NSIT Dwarka, Delhi - CPCB', 'Najafgarh, Delhi - DPCC',
       'Narela, Delhi - DPCC', 'Nehru Nagar, Delhi - DPCC',
       'North Campus, DU, Delhi - IMD', 'Okhla Phase-2, Delhi - DPCC',
       'Patparganj, Delhi - DPCC', 'Punjabi Bagh, Delhi - DPCC',
       'Pusa, Delhi - DPCC', 'Pusa, Delhi - IMD',
       'R K Puram, Delhi - DPCC', 'Rohini, Delhi - DPCC',
       'Shadipur, Delhi - CPCB', 'Sirifort, Delhi - CPCB',
       'Sonia Vihar, Delhi - DPCC', 'Sri Aurobindo Marg, Delhi - DPCC',
       'Vivek Vihar, Delhi - DPCC', 'Wazirpur, Delhi - DPCC',
       'Maninagar, Ahmedabad - GPCB', 'NISE Gwal Pahari, Gurugram - IMD',
       'Sector-51, Gurugram - HSPCB', 'Teri Gram, Gurugram - HSPCB',
       'Vikas Sadan, Gurugram - HSPCB',
       'Tata Stadium, Jorapokhar - JSPCB', 'BTM Layout, Bengaluru - CPCB',
       'BWSSB Kadabesanahalli, Bengaluru - CPCB',
       'Bapuji Nagar, Bengaluru - KSPCB',
       'City Railway Station, Bengaluru - KSPCB',
       'Hebbal, Bengaluru - KSPCB', 'Hombegowda Nagar, Bengaluru - KSPCB',
       'Jayanagar 5th Block, Bengaluru - KSPCB',
       'Peenya, Bengaluru - CPCB', 'Sanegurava Halli, Bengaluru - KSPCB',
       'Silk Board, Bengaluru - KSPCB',
       'Kariavattom, Thiruvananthapuram - Kerala PCB',
       'Plammoodu, Thiruvananthapuram - Kerala PCB',
       'T T Nagar, Bhopal - MPPCB', 'Bandra, Mumbai - MPCB',
       'Borivali East, Mumbai - MPCB',
       'Chhatrapati Shivaji Intl. Airport (T2), Mumbai - MPCB',
       'Colaba, Mumbai - MPCB', 'Kurla, Mumbai - MPCB',
       'Powai, Mumbai - MPCB', 'Sion, Mumbai - MPCB',
       'Vasai West, Mumbai - MPCB', 'Vile Parle West, Mumbai - MPCB',
       'Worli, Mumbai - MPCB', 'Lumpyngngad, Shillong - Meghalaya PCB',
       'Sikulpuikawn, Aizawl - Mizoram PCB',
       'GM Office, Brajrajnagar - OSPCB',
       'Talcher Coalfields,Talcher - OSPCB',
       'Golden Temple, Amritsar - PPCB', 'Adarsh Nagar, Jaipur - RSPCB',
       'Police Commissionerate, Jaipur - RSPCB',
       'Shastri Nagar, Jaipur - RSPCB',
       'Alandur Bus Depot, Chennai - CPCB',
       'Manali Village, Chennai - TNPCB', 'Manali, Chennai - CPCB',
       'Velachery Res. Area, Chennai - CPCB',
       'SIDCO Kurichi, Coimbatore - TNPCB',
       'Bollaram Industrial Area, Hyderabad - TSPCB',
       'Central University, Hyderabad - TSPCB',
       'ICRISAT Patancheru, Hyderabad - TSPCB',
       'IDA Pashamylaram, Hyderabad - TSPCB',
       'Sanathnagar, Hyderabad - TSPCB', 'Zoo Park, Hyderabad - TSPCB',
       'Central School, Lucknow - CPCB', 'Gomti Nagar, Lucknow - UPPCB',
       'Lalbagh, Lucknow - CPCB', 'Nishant Ganj, Lucknow - UPPCB',
       'Talkatora District Industries Center, Lucknow - CPCB',
       'Ballygunge, Kolkata - WBPCB', 'Bidhannagar, Kolkata - WBPCB',
       'Fort William, Kolkata - WBPCB', 'Jadavpur, Kolkata - WBPCB',
       'Rabindra Bharati University, Kolkata - WBPCB',
       'Rabindra Sarobar, Kolkata - WBPCB', 'Victoria, Kolkata - WBPCB'],
      dtype=object)

IHBAS, Dilshad Garden, Delhi - CPCB           2009
Manali, Chennai - CPCB                        2009
NSIT Dwarka, Delhi - CPCB                     2009
Bandra, Mumbai - MPCB                         2009
Maninagar, Ahmedabad - GPCB                   2009
                                              ... 
DRM Office Danapur, Patna - BSPCB              126
Govt. High School Shikarpur, Patna - BSPCB     121
Teri Gram, Gurugram - HSPCB                    119
Sector-51, Gurugram - HSPCB                    119
Sikulpuikawn, Aizawl - Mizoram PCB             113
Name: StationName, Length: 108, dtype: int64

	StationId	StationName	City	State	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality	Pollution content
0	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-24	71.36	115.75	1.75	20.65	12.40	12.19	0.10	10.76	109.26	0.17	5.92	0.10	184.0	Moderate	360.41
1	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-25	81.40	124.50	1.44	20.50	12.08	10.72	0.12	15.24	127.09	0.20	6.50	0.06	184.0	Moderate	399.85
2	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-26	78.32	129.06	1.26	26.00	14.85	10.28	0.14	26.96	117.44	0.22	7.95	0.08	197.0	Moderate	412.56
3	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-27	88.76	135.32	6.60	30.85	21.77	12.91	0.11	33.59	111.81	0.29	7.63	0.12	198.0	Moderate	449.76
4	AP001	Secretariat, Amaravati - APPCB	Amaravati	Andhra Pradesh	2017-11-28	64.18	104.09	2.56	28.07	17.01	11.42	0.09	19.00	138.18	0.17	5.02	0.07	188.0	Moderate	389.86
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
107706	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-27	8.65	16.46	10.27	27.25	26.62	23.65	0.69	4.36	30.59	1.32	7.26	0.40	50.0	Good	157.52
107707	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-28	11.80	18.47	13.65	200.87	214.20	11.40	0.68	3.49	38.95	1.42	7.92	0.40	65.0	Satisfactory	523.25
107708	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-29	18.60	32.26	13.65	200.87	214.20	11.40	0.78	5.12	38.17	3.52	8.64	0.40	63.0	Satisfactory	547.61
107709	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-06-30	16.07	39.30	7.56	29.13	36.69	29.26	0.69	5.88	29.64	1.86	8.40	0.40	57.0	Satisfactory	204.88
107710	WB013	Victoria, Kolkata - WBPCB	Kolkata	West Bengal	2020-07-01	10.50	36.50	7.78	22.50	30.25	27.23	0.58	2.80	13.10	1.31	7.39	0.40	59.0	Satisfactory	160.34

107711 rows × 20 columns

Plotting of various aspects of data

For curing the error- "'Series' object has no attribute 'iplot'", we're using cufflinks library.

Cities

City               0
Date               0
PM2.5           4321
PM10           10866
NO              3276
NO2             3278
NOx             3980
NH3            10061
CO              1745
SO2             3510
O3              3664
Benzene         5298
Toluene         7739
Xylene         17878
AQI             4174
Air_quality     4174
dtype: int64

	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI
count	25210.000000	18665.000000	26255.000000	26253.000000	25551.000000	19470.000000	27786.000000	26021.000000	25867.000000	24233.000000	21792.000000	11653.000000	25357.000000
mean	67.444977	118.257649	17.664483	28.488332	32.327829	23.451706	2.254926	14.658364	34.448364	3.357247	8.736095	3.101291	166.489017
std	65.132855	90.949061	23.287194	24.504528	31.770902	25.655621	7.072508	18.488735	21.743220	16.114631	20.153048	6.789023	141.084091
min	0.040000	0.010000	0.020000	0.010000	0.000000	0.010000	0.000000	0.010000	0.010000	0.000000	0.000000	0.000000	13.000000
25%	28.750000	56.200000	5.630000	11.690000	12.790000	8.490000	0.510000	5.660000	18.780000	0.120000	0.580000	0.130000	81.000000
50%	48.485000	95.710000	9.880000	21.590000	23.500000	15.800000	0.890000	9.160000	30.790000	1.070000	2.960000	0.960000	118.000000
75%	80.487500	149.890000	19.950000	37.520000	40.140000	30.000000	1.450000	15.290000	45.545000	3.090000	9.110000	3.330000	208.000000
max	949.990000	1000.000000	390.680000	362.210000	467.630000	352.890000	175.810000	193.860000	257.730000	455.030000	454.850000	170.370000	2049.000000

Replacing zeroes with median values.

City              0
Date              0
PM2.5             0
PM10              0
NO                0
NO2               0
NOx             757
NH3               0
CO             2413
SO2               0
O3                0
Benzene        3913
Toluene        2943
Xylene         1812
AQI               0
Air_quality       0
dtype: int64

Checking the data via various method for visualisation.

City           0
Date           0
PM2.5          0
PM10           0
NO             0
NO2            0
NOx            0
NH3            0
CO             0
SO2            0
O3             0
Benzene        0
Toluene        0
Xylene         0
AQI            0
Air_quality    0
dtype: int64

City           0
Date           0
PM2.5          0
PM10           0
NO             0
NO2            0
NOx            0
NH3            0
CO             0
SO2            0
O3             0
Benzene        0
Toluene        0
Xylene         0
AQI            0
Air_quality    0
dtype: int64

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 29531 entries, 0 to 29530
Data columns (total 16 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   City         29531 non-null  object        
 1   Date         29531 non-null  datetime64[ns]
 2   PM2.5        29531 non-null  float64       
 3   PM10         29531 non-null  float64       
 4   NO           29531 non-null  float64       
 5   NO2          29531 non-null  float64       
 6   NOx          29531 non-null  float64       
 7   NH3          29531 non-null  float64       
 8   CO           29531 non-null  float64       
 9   SO2          29531 non-null  float64       
 10  O3           29531 non-null  float64       
 11  Benzene      29531 non-null  float64       
 12  Toluene      29531 non-null  float64       
 13  Xylene       29531 non-null  float64       
 14  AQI          29531 non-null  float64       
 15  Air_quality  29531 non-null  object        
dtypes: datetime64[ns](1), float64(13), object(2)
memory usage: 3.6+ MB

	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI
count	29531.000000	29531.000000	29531.000000	29531.000000	29531.000000	29531.000000	29531.000000	29531.000000	29531.00000	29531.000000	29531.000000	29531.000000	29531.000000
mean	64.670738	109.961189	16.800917	27.722603	31.740472	20.844825	2.246994	14.004839	33.99446	3.088684	7.517378	1.863863	159.635434
std	60.551112	73.118152	22.093196	23.205864	29.303783	21.144911	6.849184	17.446163	20.38535	14.599926	17.397788	4.372919	131.820310
min	0.040000	0.010000	0.020000	0.010000	0.030000	0.010000	0.010000	0.010000	0.01000	0.010000	0.010000	0.010000	13.000000
25%	31.870000	78.440000	6.140000	12.770000	15.710000	11.910000	0.680000	6.040000	20.42000	0.850000	2.620000	0.960000	87.000000
50%	48.485000	95.710000	9.880000	21.590000	23.500000	15.800000	0.890000	9.160000	30.79000	1.070000	2.960000	0.960000	118.000000
75%	72.920000	112.950000	17.760000	34.820000	36.255000	22.045000	1.400000	13.955000	43.00000	2.470000	6.110000	0.960000	182.000000
max	949.990000	1000.000000	390.680000	362.210000	467.630000	352.890000	175.810000	193.860000	257.73000	455.030000	454.850000	170.370000	2049.000000

Hypothesis Testing

Visualisation of Air Quality before Covid and After Covid.

We know that during covid there is less consumption of fuels in industrial and vehicles. So, we're going to interpret the data on the basis of two types of pollutants that formed during consumption and compare the data before-COVID and after-COVID.

Before Covid

Making two types of pollutant Groups
Vehicular Pollutant = PM2.5 + PM10 + NO + NO2 + NOx + NH3 + CO
Industrial Pollutant = SO2 + O3 + Benzene + Toluene + Xylene

<class 'pandas.core.frame.DataFrame'>
Int64Index: 24908 entries, 0 to 29348
Data columns (total 7 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   City                   24908 non-null  object        
 1   Date                   24908 non-null  datetime64[ns]
 2   AQI                    24908 non-null  float64       
 3   Air_quality            24908 non-null  object        
 4   Vehicular Pollutants   24908 non-null  float64       
 5   Industrial Pollutants  24908 non-null  float64       
 6   Total Pollutants       24908 non-null  float64       
dtypes: datetime64[ns](1), float64(4), object(2)
memory usage: 1.5+ MB

Plotting some visual data for further interpretation

Plotting the Most Polluted cities in the Bar form on the basis of types of pollutants and total pollutants

Plotting the Least Polluted cities in the Bar form on the basis of types of pollutants and total pollutants

Satisfaction Level of people in the cities that are majorly polluted

	City	Date	AQI	Air_quality	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
0	Ahmedabad	2015-01-01	118.0	Moderate	197.205	163.05	360.255
1	Ahmedabad	2015-01-02	118.0	Moderate	194.085	71.56	265.645
2	Ahmedabad	2015-01-03	118.0	Moderate	243.795	85.22	329.015
3	Ahmedabad	2015-01-04	118.0	Moderate	199.845	70.24	270.085
4	Ahmedabad	2015-01-05	118.0	Moderate	263.375	107.32	370.695
...	...	...	...	...	...	...	...
29344	Visakhapatnam	2019-12-28	110.0	Moderate	240.130	65.65	305.780
29345	Visakhapatnam	2019-12-29	133.0	Moderate	180.610	93.24	273.850
29346	Visakhapatnam	2019-12-30	92.0	Satisfactory	216.790	101.37	318.160
29347	Visakhapatnam	2019-12-31	92.0	Satisfactory	222.470	94.36	316.830
29348	Visakhapatnam	2020-01-01	111.0	Moderate	235.200	92.51	327.710

24908 rows × 7 columns

Before doing the hypothesis testing we are doing the normalisation and standardisation of dataset

              Pollutants        Mean      Variance
0   Vehicular Pollutants  284.058727  28761.160744
1  Industrial Pollutants   60.332131   1715.762624
2       Total Pollutants  344.390859  35606.345506

Why is Normalization is important?

Normalization is generally required when we are dealing with attributes on a different scale, otherwise, it may lead to a dilution in effectiveness of an equally important attribute (on lower scale) because of other attribute having values on larger scale.

In simple words, when multiple attributes are there but attributes have values on different scales, this may lead to poor data models while performing data mining operations. So they are normalized to bring all the attributes on the same scale.

We're using the Standard Scaling method in which Mean is set to 0 and Variance to 1.

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
0	197.205	163.05	360.255
1	194.085	71.56	265.645
2	243.795	85.22	329.015
3	199.845	70.24	270.085
4	263.375	107.32	370.695
...	...	...	...
29344	240.130	65.65	305.780
29345	180.610	93.24	273.850
29346	216.790	101.37	318.160
29347	222.470	94.36	316.830
29348	235.200	92.51	327.710

24908 rows × 3 columns

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
0	-0.512146	2.479854	0.084074
1	-0.530544	0.271067	-0.417323
2	-0.237421	0.600852	-0.081486
3	-0.496579	0.239200	-0.393793
4	-0.121965	1.134399	0.139402

Vehicular Pollutants     2.464706e-16
Industrial Pollutants   -2.707183e-16
Total Pollutants        -3.765523e-17
dtype: float64

Vehicular Pollutants     1.00002
Industrial Pollutants    1.00002
Total Pollutants         1.00002
dtype: float64

Vehicular Pollutants     2.354917
Industrial Pollutants    2.941972
Total Pollutants         2.157560
dtype: float64

Vehicular Pollutants     2.354917
Industrial Pollutants    2.941972
Total Pollutants         2.157560
dtype: float64

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
count	24908.000000	24908.000000	24908.000000
mean	284.058727	60.332131	344.390859
std	169.591158	41.421765	188.696437
min	7.880000	3.340000	21.570000
25%	192.005000	37.290000	235.686250
50%	231.715000	46.050000	285.120000
75%	332.065000	70.880000	409.451250
max	2137.260000	776.150000	2326.750000

array([[<AxesSubplot:title={'center':'Vehicular Pollutants'}>,
        <AxesSubplot:title={'center':'Industrial Pollutants'}>],
       [<AxesSubplot:title={'center':'Total Pollutants'}>,
        <AxesSubplot:>]], dtype=object)

array([[<AxesSubplot:title={'center':'Vehicular Pollutants'}>,
        <AxesSubplot:title={'center':'Industrial Pollutants'}>],
       [<AxesSubplot:title={'center':'Total Pollutants'}>,
        <AxesSubplot:>]], dtype=object)

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
0	197.205	163.05	360.255
1	194.085	71.56	265.645
2	243.795	85.22	329.015
3	199.845	70.24	270.085
4	263.375	107.32	370.695
...	...	...	...
29344	240.130	65.65	305.780
29345	180.610	93.24	273.850
29346	216.790	101.37	318.160
29347	222.470	94.36	316.830
29348	235.200	92.51	327.710

24908 rows × 3 columns

Hypothesis Test is performed on the Total Pollutants *In which we're saying that the alpha or p_value critical is taken as 0.05 as per the data.**

We know that the if the pollutants is greater than 320 ug / m3, then it will be considered as Bad quality air.

So by considering the above statement we're taking the hypothesis:

Null Hypothesis(H0) >= 320 Alternate Hypothesis(H1) < 320

Calculating the Z Score value:

0.12925977347574807

Calculating the p-value:

0.4485760502293482

Here we're getting that p-value is greater than 0.05 which means that our Null Hypothesis is right.

This shows that the air pollution is poor before covid, which is logical also.

After Covid

Making two types of pollutant Groups
Vehicular Pollutant = PM2.5 + PM10 + NO + NO2 + NOx + NH3 + CO
Industrial Pollutant = SO2 + O3 + Benzene + Toluene + Xylene

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4623 entries, 1827 to 29530
Data columns (total 7 columns):
 #   Column                 Non-Null Count  Dtype         
---  ------                 --------------  -----         
 0   City                   4623 non-null   object        
 1   Date                   4623 non-null   datetime64[ns]
 2   AQI                    4623 non-null   float64       
 3   Air_quality            4623 non-null   object        
 4   Vehicular Pollutants   4623 non-null   float64       
 5   Industrial Pollutants  4623 non-null   float64       
 6   Total Pollutants       4623 non-null   float64       
dtypes: datetime64[ns](1), float64(4), object(2)
memory usage: 288.9+ KB

Plotting some visual data for further interpretation**

Plotting the Most Polluted cities in the Bar form on the basis of types of pollutants and total pollutants

Plotting the Least Polluted cities in the Bar form on the basis of types of pollutants and total pollutants

Satisfaction Level of people in the cities that are majorly polluted

	City	Date	AQI	Air_quality	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
1827	Ahmedabad	2020-01-02	162.0	Moderate	248.62	85.00	333.62
1828	Ahmedabad	2020-01-03	220.0	Poor	256.23	97.88	354.11
1829	Ahmedabad	2020-01-04	254.0	Poor	276.04	100.41	376.45
1830	Ahmedabad	2020-01-05	255.0	Poor	219.89	106.40	326.29
1831	Ahmedabad	2020-01-06	175.0	Moderate	217.00	98.16	315.16
...	...	...	...	...	...	...	...
29526	Visakhapatnam	2020-06-27	41.0	Good	131.18	46.89	178.07
29527	Visakhapatnam	2020-06-28	70.0	Satisfactory	156.99	46.19	203.18
29528	Visakhapatnam	2020-06-29	68.0	Satisfactory	151.14	40.36	191.50
29529	Visakhapatnam	2020-06-30	54.0	Satisfactory	129.27	43.13	172.40
29530	Visakhapatnam	2020-07-01	50.0	Good	128.09	24.14	152.23

4623 rows × 7 columns

Before doing the hypothesis testing we are doing the normalisation and standardisation of dataset

              Pollutants        Mean      Variance
0   Vehicular Pollutants  219.726823  18957.688067
1  Industrial Pollutants   61.207863   4191.823405
2       Total Pollutants  280.934686  23610.013374

We're using the Standard Scaling method in which Mean is set to 0 and Variance to 1.

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
1827	248.62	85.00	333.62
1828	256.23	97.88	354.11
1829	276.04	100.41	376.45
1830	219.89	106.40	326.29
1831	217.00	98.16	315.16
...	...	...	...
29526	131.18	46.89	178.07
29527	156.99	46.19	203.18
29528	151.14	40.36	191.50
29529	129.27	43.13	172.40
29530	128.09	24.14	152.23

4623 rows × 3 columns

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
0	0.209870	0.367518	0.342917
1	0.265146	0.566476	0.476281
2	0.409039	0.605557	0.621687
3	0.001185	0.698085	0.295207
4	-0.019807	0.570801	0.222765

Vehicular Pollutants     1.229579e-17
Industrial Pollutants   -4.303525e-17
Total Pollutants         6.762682e-17
dtype: float64

Vehicular Pollutants     1.000108
Industrial Pollutants    1.000108
Total Pollutants         1.000108
dtype: float64

Vehicular Pollutants      1.872608
Industrial Pollutants    10.533733
Total Pollutants          1.835976
dtype: float64

Vehicular Pollutants      1.872608
Industrial Pollutants    10.533733
Total Pollutants          1.835976
dtype: float64

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
count	4623.000000	4623.000000	4623.000000
mean	219.726823	61.207863	280.934686
std	137.686920	64.744292	153.655502
min	15.790000	3.980000	38.910000
25%	126.520000	37.800000	174.445000
50%	189.730000	50.840000	251.860000
75%	270.150000	71.225000	342.095000
max	1266.900000	969.380000	1298.770000

array([[<AxesSubplot:title={'center':'Vehicular Pollutants'}>,
        <AxesSubplot:title={'center':'Industrial Pollutants'}>],
       [<AxesSubplot:title={'center':'Total Pollutants'}>,
        <AxesSubplot:>]], dtype=object)

array([[<AxesSubplot:title={'center':'Vehicular Pollutants'}>,
        <AxesSubplot:title={'center':'Industrial Pollutants'}>],
       [<AxesSubplot:title={'center':'Total Pollutants'}>,
        <AxesSubplot:>]], dtype=object)

	Vehicular Pollutants	Industrial Pollutants	Total Pollutants
1827	248.62	85.00	333.62
1828	256.23	97.88	354.11
1829	276.04	100.41	376.45
1830	219.89	106.40	326.29
1831	217.00	98.16	315.16
...	...	...	...
29526	131.18	46.89	178.07
29527	156.99	46.19	203.18
29528	151.14	40.36	191.50
29529	129.27	43.13	172.40
29530	128.09	24.14	152.23

4623 rows × 3 columns

Hypothesis Test is performed on the Total Pollutants *In which we're saying that the alpha or p_value critical is taken as 0.05 as per the data.**

We know that the if the pollutants is greater than 320 ug / m3, then it will be considered as Bad quality air.

So by considering the above statement we're taking the hypothesis:

Null Hypothesis(H0) >= 320 Alternate Hypothesis(H1) < 320

Calculating the Z-Score value:

-0.25423960141421537

Calculating the p-value:

0.39965522897962547

Here we're getting that p-value is greater than 0.05 which means that our Null Hypothesis is right.

This shows that the air pollution is poor after covid also, which is again logical.

Predicting the AQI

Here, we're going to predict the AQI in two ways:
1) Calculating the AQI as whole of India yearly by using Linear Regression model
2) Calculating the AQI for each city for upcoming years

1. Calculating the AQI as whole of India yearly by using Linear Regression

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
0	Ahmedabad	2015-01-01	48.485	95.71	0.92	18.22	17.15	15.80	0.92	27.64	133.36	1.07	0.02	0.96	118.0	Moderate
1	Ahmedabad	2015-01-02	48.485	95.71	0.97	15.69	16.46	15.80	0.97	24.55	34.06	3.68	5.50	3.77	118.0	Moderate
2	Ahmedabad	2015-01-03	48.485	95.71	17.40	19.30	29.70	15.80	17.40	29.07	30.70	6.80	16.40	2.25	118.0	Moderate
3	Ahmedabad	2015-01-04	48.485	95.71	1.70	18.48	17.97	15.80	1.70	18.59	36.08	4.43	10.14	1.00	118.0	Moderate
4	Ahmedabad	2015-01-05	48.485	95.71	22.10	21.42	37.76	15.80	22.10	39.33	39.31	7.01	18.89	2.78	118.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29526	Visakhapatnam	2020-06-27	15.020	50.94	7.68	25.06	19.54	12.47	0.47	8.55	23.30	2.24	12.07	0.73	41.0	Good
29527	Visakhapatnam	2020-06-28	24.380	74.09	3.42	26.06	16.53	11.99	0.52	12.72	30.14	0.74	2.21	0.38	70.0	Satisfactory
29528	Visakhapatnam	2020-06-29	22.910	65.73	3.45	29.53	18.33	10.71	0.48	8.42	30.96	0.01	0.01	0.96	68.0	Satisfactory
29529	Visakhapatnam	2020-06-30	16.640	49.97	4.05	29.26	18.80	10.03	0.52	9.84	28.30	1.07	2.96	0.96	54.0	Satisfactory
29530	Visakhapatnam	2020-07-01	15.000	66.00	0.40	26.85	14.05	5.20	0.59	2.10	17.05	1.07	2.96	0.96	50.0	Good

29531 rows × 16 columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 29531 entries, 0 to 29530
Data columns (total 16 columns):
 #   Column       Non-Null Count  Dtype         
---  ------       --------------  -----         
 0   City         29531 non-null  object        
 1   Date         29531 non-null  datetime64[ns]
 2   PM2.5        29531 non-null  float64       
 3   PM10         29531 non-null  float64       
 4   NO           29531 non-null  float64       
 5   NO2          29531 non-null  float64       
 6   NOx          29531 non-null  float64       
 7   NH3          29531 non-null  float64       
 8   CO           29531 non-null  float64       
 9   SO2          29531 non-null  float64       
 10  O3           29531 non-null  float64       
 11  Benzene      29531 non-null  float64       
 12  Toluene      29531 non-null  float64       
 13  Xylene       29531 non-null  float64       
 14  AQI          29531 non-null  float64       
 15  Air_quality  29531 non-null  object        
dtypes: datetime64[ns](1), float64(13), object(2)
memory usage: 3.6+ MB

	Date	Month	Year	AQI_avg
0	2015-01-01	1	2015	177.000000
1	2015-01-02	1	2015	174.000000
2	2015-01-03	1	2015	122.166667
3	2015-01-04	1	2015	146.714286
4	2015-01-05	1	2015	147.571429
...	...	...	...	...
2004	2020-06-27	6	2020	74.346154
2005	2020-06-28	6	2020	79.038462
2006	2020-06-29	6	2020	78.000000
2007	2020-06-30	6	2020	72.230769
2008	2020-07-01	7	2020	89.615385

2009 rows × 4 columns

Predicting the AQI for taking all years in consideration

	Date	Month	Year	AQI_avg
0	2015-01-01	1	2015	177.000000
1	2015-01-02	1	2015	174.000000
2	2015-01-03	1	2015	122.166667
3	2015-01-04	1	2015	146.714286
4	2015-01-05	1	2015	147.571429
...	...	...	...	...
2004	2020-06-27	6	2020	74.346154
2005	2020-06-28	6	2020	79.038462
2006	2020-06-29	6	2020	78.000000
2007	2020-06-30	6	2020	72.230769
2008	2020-07-01	7	2020	89.615385

2009 rows × 4 columns

<AxesSubplot:xlabel='AQI_avg'>

array([[ 1.        ,  1.33630621],
       [ 1.        ,  0.80178373],
       [ 1.        ,  0.26726124],
       [ 1.        , -0.26726124],
       [ 1.        , -0.80178373],
       [ 1.        , -1.33630621]])

Gradient Descent: 161.82, -20.54

	Year	AQI_avg	Actual	Predicted
5	2020	114.927696	114.927696	134.372270
4	2019	156.116369	156.116369	145.351362
3	2018	176.373409	176.373409	156.330454
2	2017	164.348404	164.348404	167.309546
1	2016	178.330358	178.330358	178.288638
0	2015	180.848065	180.848065	189.267730

12.749887150652524

Predicting the AQI by taking the years before COVID-19

	Date	Month	Year	AQI_avg
0	2015-01-01	1	2015	177.000000
1	2015-01-02	1	2015	174.000000
2	2015-01-03	1	2015	122.166667
3	2015-01-04	1	2015	146.714286
4	2015-01-05	1	2015	147.571429
...	...	...	...	...
1822	2019-12-28	12	2019	183.434783
1823	2019-12-29	12	2019	204.304348
1824	2019-12-30	12	2019	221.565217
1825	2019-12-31	12	2019	204.956522
1826	2020-01-01	1	2020	211.217391

1827 rows × 4 columns

<AxesSubplot:xlabel='AQI_avg'>

array([[ 1.        ,  1.33630621],
       [ 1.        ,  0.80178373],
       [ 1.        ,  0.26726124],
       [ 1.        , -0.26726124],
       [ 1.        , -0.80178373],
       [ 1.        , -1.33630621]])

Gradient Descent: 177.87, 5.20

	Year	AQI_avg	Actual	Predicted
5	2020	114.927696	211.217391	184.818792
4	2019	156.116369	156.116369	182.039275
3	2018	176.373409	176.373409	179.259758
2	2017	164.348404	164.348404	176.480242
1	2016	178.330358	178.330358	173.700725
0	2015	180.848065	180.848065	170.921208

16.55481596210976

Predicting the AQI by taking the years after COVID-19

	Date	Month	Year	AQI_avg
1826	2020-01-01	1	2020	211.217391
1827	2020-01-02	1	2020	187.260870
1828	2020-01-03	1	2020	163.739130
1829	2020-01-04	1	2020	146.956522
1830	2020-01-05	1	2020	160.521739
...	...	...	...	...
2004	2020-06-27	6	2020	74.346154
2005	2020-06-28	6	2020	79.038462
2006	2020-06-29	6	2020	78.000000
2007	2020-06-30	6	2020	72.230769
2008	2020-07-01	7	2020	89.615385

183 rows × 4 columns

<class 'pandas.core.frame.DataFrame'>
Int64Index: 183 entries, 1826 to 2008
Data columns (total 4 columns):
 #   Column   Non-Null Count  Dtype         
---  ------   --------------  -----         
 0   Date     183 non-null    datetime64[ns]
 1   Month    183 non-null    int64         
 2   Year     183 non-null    int64         
 3   AQI_avg  183 non-null    float64       
dtypes: datetime64[ns](1), float64(1), int64(2)
memory usage: 7.1 KB

<AxesSubplot:xlabel='AQI_avg'>

array([[ 1.        ,  1.38873015],
       [ 1.        ,  0.9258201 ],
       [ 1.        ,  0.46291005],
       [ 1.        ,  0.        ],
       [ 1.        , -0.46291005],
       [ 1.        , -0.9258201 ],
       [ 1.        , -1.38873015]])

Gradient Descent: 111.53, -31.99

	Year	AQI_avg	Actual	Predicted
0	2015.0	180.848065	167.450940	155.955477
1	2016.0	178.330358	157.586207	141.146985
2	2017.0	164.348404	110.869132	126.338492
3	2018.0	176.373409	88.646154	111.530000
4	2019.0	156.116369	88.243176	96.721508
5	2020.0	114.927696	78.310256	81.913015
6	NaN	NaN	89.615385	67.104523

15.84282666114951

2. Calculating the AQI for each cities for upcoming years

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
0	Ahmedabad	2015-01-01	48.485	95.71	0.92	18.22	17.15	15.80	0.92	27.64	133.36	1.07	0.02	0.96	118.0	Moderate
1	Ahmedabad	2015-01-02	48.485	95.71	0.97	15.69	16.46	15.80	0.97	24.55	34.06	3.68	5.50	3.77	118.0	Moderate
2	Ahmedabad	2015-01-03	48.485	95.71	17.40	19.30	29.70	15.80	17.40	29.07	30.70	6.80	16.40	2.25	118.0	Moderate
3	Ahmedabad	2015-01-04	48.485	95.71	1.70	18.48	17.97	15.80	1.70	18.59	36.08	4.43	10.14	1.00	118.0	Moderate
4	Ahmedabad	2015-01-05	48.485	95.71	22.10	21.42	37.76	15.80	22.10	39.33	39.31	7.01	18.89	2.78	118.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29526	Visakhapatnam	2020-06-27	15.020	50.94	7.68	25.06	19.54	12.47	0.47	8.55	23.30	2.24	12.07	0.73	41.0	Good
29527	Visakhapatnam	2020-06-28	24.380	74.09	3.42	26.06	16.53	11.99	0.52	12.72	30.14	0.74	2.21	0.38	70.0	Satisfactory
29528	Visakhapatnam	2020-06-29	22.910	65.73	3.45	29.53	18.33	10.71	0.48	8.42	30.96	0.01	0.01	0.96	68.0	Satisfactory
29529	Visakhapatnam	2020-06-30	16.640	49.97	4.05	29.26	18.80	10.03	0.52	9.84	28.30	1.07	2.96	0.96	54.0	Satisfactory
29530	Visakhapatnam	2020-07-01	15.000	66.00	0.40	26.85	14.05	5.20	0.59	2.10	17.05	1.07	2.96	0.96	50.0	Good

29531 rows × 16 columns

array(['Ahmedabad', 'Aizawl', 'Amaravati', 'Amritsar', 'Bengaluru',
       'Bhopal', 'Brajrajnagar', 'Chandigarh', 'Chennai', 'Coimbatore',
       'Delhi', 'Ernakulam', 'Gurugram', 'Guwahati', 'Hyderabad',
       'Jaipur', 'Jorapokhar', 'Kochi', 'Kolkata', 'Lucknow', 'Mumbai',
       'Patna', 'Shillong', 'Talcher', 'Thiruvananthapuram',
       'Visakhapatnam'], dtype=object)

Ahmedabad             2009
Delhi                 2009
Mumbai                2009
Bengaluru             2009
Lucknow               2009
Chennai               2009
Hyderabad             2006
Patna                 1858
Gurugram              1679
Visakhapatnam         1462
Amritsar              1221
Jorapokhar            1169
Jaipur                1114
Thiruvananthapuram    1112
Amaravati              951
Brajrajnagar           938
Talcher                925
Kolkata                814
Guwahati               502
Coimbatore             386
Shillong               310
Chandigarh             304
Bhopal                 289
Ernakulam              162
Kochi                  162
Aizawl                 113
Name: City, dtype: int64

The AQI of India seems to vary sporadically between local regions but,as we saw, possesses a seasonal rally trend in the monsoon. For this reason, Prophet was chosen as it has excellent seasonality learning capabilities in time-series analaysis.

I) Forecasting Delhi's AQI for the upcoming years by using the dataset available

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
10229	Delhi	2015-01-01	313.22	607.98	69.16	36.39	110.59	33.85	15.20	9.25	41.68	14.36	24.86	9.84	472.0	Severe
10230	Delhi	2015-01-02	186.18	269.55	62.09	32.87	88.14	31.83	9.54	6.65	29.97	10.55	20.09	4.29	454.0	Severe
10231	Delhi	2015-01-03	87.18	131.90	25.73	30.31	47.95	69.55	10.61	2.65	19.71	3.91	10.23	1.99	143.0	Moderate
10232	Delhi	2015-01-04	151.84	241.84	25.01	36.91	48.62	130.36	11.54	4.63	25.36	4.26	9.71	3.34	319.0	Very Poor
10233	Delhi	2015-01-05	146.60	219.13	14.01	34.92	38.25	122.88	9.20	3.33	23.20	2.80	6.21	2.96	325.0	Very Poor
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
12233	Delhi	2020-06-27	39.80	155.94	10.88	21.46	22.47	31.43	0.87	10.38	18.88	1.69	19.99	0.43	112.0	Moderate
12234	Delhi	2020-06-28	59.52	308.65	12.67	21.60	23.86	29.27	0.94	10.70	18.05	1.71	25.13	1.74	196.0	Moderate
12235	Delhi	2020-06-29	44.86	184.12	10.50	21.57	21.94	27.97	0.88	11.58	26.61	2.13	23.80	1.13	233.0	Poor
12236	Delhi	2020-06-30	39.80	91.98	5.99	17.96	15.44	28.48	0.84	10.51	37.29	1.57	16.37	0.49	114.0	Moderate
12237	Delhi	2020-07-01	54.01	128.66	6.33	21.05	16.81	29.06	0.97	11.15	29.73	2.03	23.57	0.65	101.0	Moderate

2009 rows × 16 columns

	Date	AQI
0	2015-01-01	472.0
1	2015-01-02	454.0
2	2015-01-03	143.0
3	2015-01-04	319.0
4	2015-01-05	325.0
...	...	...
2004	2020-06-27	112.0
2005	2020-06-28	196.0
2006	2020-06-29	233.0
2007	2020-06-30	114.0
2008	2020-07-01	101.0

2009 rows × 2 columns

01:18:43 - cmdstanpy - INFO - Chain [1] start processing
01:18:44 - cmdstanpy - INFO - Chain [1] done processing

<prophet.forecaster.Prophet at 0x1ee27236140>

	ds
2369	2021-06-27
2370	2021-06-28
2371	2021-06-29
2372	2021-06-30
2373	2021-07-01

	ds	yhat	yhat_lower	yhat_upper
2369	2021-06-27	15.151967	-72.179657	107.341479
2370	2021-06-28	8.304171	-81.092528	98.563748
2371	2021-06-29	7.531971	-81.042863	95.885354
2372	2021-06-30	8.359923	-80.456249	92.903216
2373	2021-07-01	8.126453	-76.932192	108.591637

  0%|          | 0/5 [00:00<?, ?it/s]

01:18:56 - cmdstanpy - INFO - Chain [1] start processing
01:18:56 - cmdstanpy - INFO - Chain [1] done processing
01:19:06 - cmdstanpy - INFO - Chain [1] start processing
01:19:07 - cmdstanpy - INFO - Chain [1] done processing
01:19:16 - cmdstanpy - INFO - Chain [1] start processing
01:19:17 - cmdstanpy - INFO - Chain [1] done processing
01:19:25 - cmdstanpy - INFO - Chain [1] start processing
01:19:26 - cmdstanpy - INFO - Chain [1] done processing
01:19:36 - cmdstanpy - INFO - Chain [1] start processing
01:19:36 - cmdstanpy - INFO - Chain [1] done processing

Cross Validation accuracy: 67.97047760703944

By using this model we are getting the accuracy of 67.97 for Delhi which is really good for using this model. This model can be used for predicting the trends in the AQI for Delhi of upcoming years.

Printing the trend of AQI in Delhi for upcoming years and finding the yearly, monthly, and weekly behaviour.

II) Forecasting Patna's AQI for the upcoming years by using the dataset available

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
23864	Patna	2015-06-01	48.485	95.71	14.41	25.06	39.32	15.80	1.56	1.80	8.89	1.07	0.29	0.96	118.0	Moderate
23865	Patna	2015-06-02	48.485	95.71	25.00	22.48	47.50	15.80	2.35	9.69	9.90	0.08	0.83	0.09	118.0	Moderate
23866	Patna	2015-06-03	48.485	95.71	14.29	17.16	29.81	15.80	1.69	20.61	12.63	1.07	0.33	0.96	118.0	Moderate
23867	Patna	2015-06-04	48.485	95.71	13.03	15.62	28.63	15.80	1.20	4.35	9.77	0.01	0.28	0.96	118.0	Moderate
23868	Patna	2015-06-05	48.485	95.71	10.40	10.36	20.14	15.80	1.29	7.22	11.90	1.07	0.15	0.96	118.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
25717	Patna	2020-06-27	17.710	63.73	9.47	23.01	22.28	1.91	0.87	3.63	23.39	1.09	3.07	0.97	65.0	Satisfactory
25718	Patna	2020-06-28	19.270	57.42	30.19	18.13	36.76	2.05	0.72	3.92	17.37	1.18	2.90	1.24	82.0	Satisfactory
25719	Patna	2020-06-29	17.240	42.83	42.40	20.51	47.69	2.26	0.88	3.60	17.50	1.51	4.91	1.74	88.0	Satisfactory
25720	Patna	2020-06-30	29.760	60.68	42.12	27.50	52.04	1.59	0.83	3.91	21.70	1.58	8.59	2.02	93.0	Satisfactory
25721	Patna	2020-07-01	35.420	57.82	44.50	31.15	57.72	1.14	0.82	3.99	25.76	1.73	5.50	2.14	98.0	Satisfactory

1858 rows × 16 columns

01:19:47 - cmdstanpy - INFO - Chain [1] start processing
01:19:48 - cmdstanpy - INFO - Chain [1] done processing

<prophet.forecaster.Prophet at 0x1ee26e83cd0>

	ds
2218	2021-06-27
2219	2021-06-28
2220	2021-06-29
2221	2021-06-30
2222	2021-07-01

	ds	yhat	yhat_lower	yhat_upper
2218	2021-06-27	11.754445	-62.226604	82.207675
2219	2021-06-28	8.512360	-67.814273	82.807106
2220	2021-06-29	13.255852	-59.275857	86.541985
2221	2021-06-30	13.006771	-59.560854	85.133394
2222	2021-07-01	9.804651	-63.738632	83.167927

  0%|          | 0/4 [00:00<?, ?it/s]

01:19:59 - cmdstanpy - INFO - Chain [1] start processing
01:19:59 - cmdstanpy - INFO - Chain [1] done processing
01:20:09 - cmdstanpy - INFO - Chain [1] start processing
01:20:10 - cmdstanpy - INFO - Chain [1] done processing
01:20:17 - cmdstanpy - INFO - Chain [1] start processing
01:20:17 - cmdstanpy - INFO - Chain [1] done processing
01:20:27 - cmdstanpy - INFO - Chain [1] start processing
01:20:28 - cmdstanpy - INFO - Chain [1] done processing

Cross Validation accuracy: 63.38596674035115

By using this model we are getting the accuracy of 63.386 for Patna which is really good for using this model. This model can be used for predicting the trends in the AQI for Patna of upcoming years.

Printing the trend of AQI in Patna for upcoming years and finding the yearly, monthly, and weekly behaviour.

III) Forecasting Bengaluru's AQI for the upcoming years by using the dataset available

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
4294	Bengaluru	2015-01-01	48.485	95.71	3.26	17.33	10.88	20.36	0.33	3.54	10.73	0.56	4.64	0.96	118.0	Moderate
4295	Bengaluru	2015-01-02	48.485	95.71	6.05	19.73	14.14	23.74	1.35	3.97	22.77	0.65	5.31	0.96	118.0	Moderate
4296	Bengaluru	2015-01-03	48.485	95.71	11.91	19.88	20.72	4.32	17.40	13.61	12.03	0.53	19.25	0.96	118.0	Moderate
4297	Bengaluru	2015-01-04	48.485	95.71	7.45	21.61	16.88	0.87	5.05	6.52	17.70	0.55	7.47	0.96	118.0	Moderate
4298	Bengaluru	2015-01-05	48.485	95.71	9.52	22.17	21.76	31.38	1.83	4.71	12.72	0.40	4.36	0.96	118.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6298	Bengaluru	2020-06-27	16.600	29.48	3.06	13.68	13.07	6.88	0.67	7.29	15.69	0.21	1.18	0.96	51.0	Satisfactory
6299	Bengaluru	2020-06-28	20.440	26.34	2.69	10.33	10.58	6.58	0.66	6.60	17.59	0.12	0.94	0.96	61.0	Satisfactory
6300	Bengaluru	2020-06-29	28.680	29.27	3.62	12.12	12.94	6.80	0.56	6.33	16.99	0.17	1.17	0.96	65.0	Satisfactory
6301	Bengaluru	2020-06-30	14.470	24.26	4.61	12.69	15.00	6.82	0.56	6.45	16.08	0.18	0.86	0.96	63.0	Satisfactory
6302	Bengaluru	2020-07-01	17.500	30.48	3.95	13.25	14.83	7.42	0.54	6.66	15.40	0.27	0.65	0.96	43.0	Good

2009 rows × 16 columns

01:20:40 - cmdstanpy - INFO - Chain [1] start processing
01:20:40 - cmdstanpy - INFO - Chain [1] done processing

<prophet.forecaster.Prophet at 0x1ee2cf6f580>

	ds
2369	2021-06-27
2370	2021-06-28
2371	2021-06-29
2372	2021-06-30
2373	2021-07-01

	ds	yhat	yhat_lower	yhat_upper
2369	2021-06-27	51.442944	8.732539	93.997069
2370	2021-06-28	51.458122	8.943406	94.890603
2371	2021-06-29	54.972129	9.719688	98.774818
2372	2021-06-30	54.693472	14.603014	99.555499
2373	2021-07-01	53.862229	11.849875	96.461272

  0%|          | 0/5 [00:00<?, ?it/s]

01:20:52 - cmdstanpy - INFO - Chain [1] start processing
01:20:53 - cmdstanpy - INFO - Chain [1] done processing
01:21:03 - cmdstanpy - INFO - Chain [1] start processing
01:21:03 - cmdstanpy - INFO - Chain [1] done processing
01:21:12 - cmdstanpy - INFO - Chain [1] start processing
01:21:13 - cmdstanpy - INFO - Chain [1] done processing
01:21:22 - cmdstanpy - INFO - Chain [1] start processing
01:21:23 - cmdstanpy - INFO - Chain [1] done processing
01:21:32 - cmdstanpy - INFO - Chain [1] start processing
01:21:33 - cmdstanpy - INFO - Chain [1] done processing

Cross Validation accuracy: 63.49849589422245

By using this model we are getting the accuracy of 63.498 for Bengaluru which is really good for using this model. This model can be used for predicting the trends in the AQI for Bengaluru of upcoming years.

Printing the trend of AQI in Bengaluru for upcoming years and finding the yearly, monthly, and weekly behaviour.

IV) Forecasting Chennai's AQI for the upcoming years by using the dataset available

	City	Date	PM2.5	PM10	NO	NO2	NOx	NH3	CO	SO2	O3	Benzene	Toluene	Xylene	AQI	Air_quality
7834	Chennai	2015-01-01	48.485	95.71	16.30	15.39	22.68	4.59	1.17	9.20	11.35	0.17	2.96	0.96	118.0	Moderate
7835	Chennai	2015-01-02	48.485	95.71	16.49	13.42	23.09	7.83	1.23	8.61	9.16	0.13	2.96	0.96	118.0	Moderate
7836	Chennai	2015-01-03	48.485	95.71	9.72	19.56	9.99	4.63	0.77	48.23	13.45	0.03	2.96	0.96	118.0	Moderate
7837	Chennai	2015-01-04	48.485	95.71	9.60	16.20	11.71	5.23	1.00	27.96	10.33	1.07	2.96	0.96	118.0	Moderate
7838	Chennai	2015-01-05	48.485	95.71	9.16	16.30	12.94	5.50	0.90	16.60	9.36	0.57	2.96	0.96	118.0	Moderate
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9838	Chennai	2020-06-27	26.420	39.30	7.25	12.96	19.59	33.20	1.10	7.29	68.51	0.10	0.07	0.96	95.0	Satisfactory
9839	Chennai	2020-06-28	25.930	45.54	7.81	10.00	16.39	35.98	0.76	6.48	77.45	0.09	2.96	0.96	98.0	Satisfactory
9840	Chennai	2020-06-29	21.300	22.21	7.65	9.69	16.74	34.07	0.96	6.62	62.57	0.09	0.01	0.96	104.0	Moderate
9841	Chennai	2020-06-30	24.140	30.66	8.42	12.38	20.29	34.17	1.05	7.50	68.75	0.17	0.16	0.96	110.0	Moderate
9842	Chennai	2020-07-01	15.950	4.85	6.22	10.72	16.44	33.52	1.02	9.23	48.37	0.09	2.96	0.96	92.0	Satisfactory

2009 rows × 16 columns

01:21:45 - cmdstanpy - INFO - Chain [1] start processing
01:21:46 - cmdstanpy - INFO - Chain [1] done processing

<prophet.forecaster.Prophet at 0x1ee27552b30>

	ds
2369	2021-06-27
2370	2021-06-28
2371	2021-06-29
2372	2021-06-30
2373	2021-07-01

	ds	yhat	yhat_lower	yhat_upper
2369	2021-06-27	65.917531	4.447421	124.194548
2370	2021-06-28	63.813983	5.102808	127.140649
2371	2021-06-29	69.038115	15.507342	127.347812
2372	2021-06-30	73.645892	16.467333	135.093176
2373	2021-07-01	73.117267	14.157471	128.271758

  0%|          | 0/5 [00:00<?, ?it/s]

01:21:57 - cmdstanpy - INFO - Chain [1] start processing
01:21:57 - cmdstanpy - INFO - Chain [1] done processing
01:22:07 - cmdstanpy - INFO - Chain [1] start processing
01:22:07 - cmdstanpy - INFO - Chain [1] done processing
01:22:17 - cmdstanpy - INFO - Chain [1] start processing
01:22:17 - cmdstanpy - INFO - Chain [1] done processing
01:22:27 - cmdstanpy - INFO - Chain [1] start processing
01:22:28 - cmdstanpy - INFO - Chain [1] done processing
01:22:37 - cmdstanpy - INFO - Chain [1] start processing
01:22:37 - cmdstanpy - INFO - Chain [1] done processing

Cross Validation accuracy: 61.76724564183594

By using this model we are getting the accuracy of 61.767 for Chennai which is really good for using this model. This model can be used for predicting the trends in the AQI for Chennai of upcoming years.

Printing the trend of AQI in Chennai for upcoming years and finding the yearly, monthly, and weekly behaviour.

From the above examples of 4 cities, by using this model, we are getting a cross-validation accuracy above 60. This shows that we can implement this model for predicting or forecasting the behaviour of AQI in future for various cities across India.